Zobrazeno 1 - 10
of 626
pro vyhledávání: '"Huang, Heyan"'
Domain adaptation aims to enable Large Language Models (LLMs) to generalize domain datasets unseen effectively during the training phase. However, factors such as the size of the model parameters and the scale of training data are general influencers
Externí odkaz:
http://arxiv.org/abs/2406.14828
It is crucial to utilize events to understand a specific domain. There are lots of research on event extraction in many domains such as news, finance and biology domain. However, scientific domain still lacks event extraction research, including comp
Externí odkaz:
http://arxiv.org/abs/2406.14075
While large language models (LLMs) have made notable advancements in natural language processing, they continue to struggle with processing extensive text. Memory mechanism offers a flexible solution for managing long contexts, utilizing techniques s
Externí odkaz:
http://arxiv.org/abs/2406.13167
Recent studies have demonstrated that In-Context Learning (ICL), through the use of specific demonstrations, can align Large Language Models (LLMs) with human preferences known as In-Context Alignment (ICA), indicating that models can comprehend huma
Externí odkaz:
http://arxiv.org/abs/2406.11474
Autor:
Bai, Yu, Zou, Xiyuan, Huang, Heyan, Chen, Sanxing, Rondeau, Marc-Antoine, Gao, Yang, Cheung, Jackie Chi Kit
Long sequence modeling has gained broad interest as large language models (LLMs) continue to advance. Recent research has identified that a large portion of hidden states within the key-value caches of Transformer models can be discarded (also termed
Externí odkaz:
http://arxiv.org/abs/2406.12018
Data augmentation is an effective way to diversify corpora in machine translation, but previous methods may introduce semantic inconsistency between original and augmented data because of irreversible operations and random subword sampling procedures
Externí odkaz:
http://arxiv.org/abs/2406.02517
The recent success of large language models (LLMs) has attracted widespread interest to develop role-playing conversational agents personalized to the characteristics and styles of different speakers to enhance their abilities to perform both general
Externí odkaz:
http://arxiv.org/abs/2405.10150
Retrieval-Augmented Language Modeling (RALM) by integrating large language models (LLM) with relevant documents from an external corpus is a proven method for enabling the LLM to generate information beyond the scope of its pre-training corpus. Previ
Externí odkaz:
http://arxiv.org/abs/2405.04065
Mixed initiative serves as one of the key factors in controlling conversation directions. For a speaker, responding passively or leading proactively would result in rather different responses. However, most dialogue systems focus on training a holist
Externí odkaz:
http://arxiv.org/abs/2403.17636
Autor:
Zhuo, Le, Chi, Zewen, Xu, Minghao, Huang, Heyan, Zheng, Heqi, He, Conghui, Mao, Xian-Ling, Zhang, Wentao
We propose ProtLLM, a versatile cross-modal large language model (LLM) for both protein-centric and protein-language tasks. ProtLLM features a unique dynamic protein mounting mechanism, enabling it to handle complex inputs where the natural language
Externí odkaz:
http://arxiv.org/abs/2403.07920